Systems and methods for determining video feature descriptors based on convolutional neural networks

ABSTRACT

Systems, methods, and non-transitory computer-readable media can acquire video content for which video feature descriptors are to be determined. The video content can be processed based at least in part on a convolutional neural network including a set of two-dimensional convolutional layers and a set of three-dimensional convolutional layers. One or more outputs can be generated from the convolutional neural network. A plurality of video feature descriptors for the video content can be determined based at least in part on the one or more outputs from the convolutional neural network.

FIELD OF THE INVENTION

The present technology relates to the field of media processing. Moreparticularly, the present technology relates to techniques fordetermining video feature descriptors based on convolutional neuralnetworks.

BACKGROUND

Today, people often utilize computing devices or systems for a widevariety of purposes. For example, users can use their computing devices(or systems) to interact with one another, create content, shareinformation, and access information. In some instances, a user of acomputing device can utilize a camera or other image sensor of thecomputing device to capture or record media content, such as videocontent. Sometimes users have to manually provide information, such asby manually inputting descriptions and tags (e.g., identifier tags,location tags, hashtags, etc.), in order to describe the video content.

In some instances, media content can be analyzed by computing devices orsystems in attempt to identify items, subjects, or other objects thatare represented or included in the media content. In one example, imagescan be analyzed to detect one or more faces in each of the images. Inanother example, an image can be analyzed to identify any productswithin the image that are available for purchase via an onlinestorefront. However, conventional approaches for recognizing objectswithin media content can often times be inefficient, inaccurate, andlimited in capability. Due to these and other reasons, conventionalapproaches can create challenges for or reduce the overall userexperience associated with media content interaction.

SUMMARY

Various embodiments of the present disclosure can include systems,methods, and non-transitory computer readable media configured toacquire video content for which video feature descriptors are to bedetermined. The video content can be processed based at least in part ona convolutional neural network including a set of two-dimensionalconvolutional layers and a set of three-dimensional convolutionallayers. One or more outputs can be generated from the convolutionalneural network. A plurality of video feature descriptors for the videocontent can be determined based at least in part on the one or moreoutputs from the convolutional neural network.

In an embodiment, the video content can be represented as a plurality oftwo-dimensional image frames. Each of the plurality of two-dimensionalimage frames can extend in a first spatial dimension and a secondspatial dimension. The plurality of two-dimensional image frames can betemporally sorted. A third dimension can correspond to a time dimensionwith respect to which the plurality of two-dimensional image frames istemporally sorted.

In an embodiment, the processing of the video content based at least inpart on the convolutional neural network can further comprise inputtinga representation of the video content into the set of two-dimensionalconvolutional layers. At least one two-dimensional convolutionaloperation can be applied, within the set of two-dimensionalconvolutional layers, to the representation of the video content. Afirst collection of signals can be outputted from the set oftwo-dimensional convolutional layers. At least a portion of the firstcollection of signals can be inputted into the set of three-dimensionalconvolutional layers. At least one three-dimensional convolutionaloperation can be applied, within the set of three-dimensionalconvolutional layers, to at least the portion of the first collection ofsignals. A second collection of signals can be outputted from the set ofthree-dimensional convolutional layers. The one or more outputs from theconvolutional neural network can be dependent on at least a portion ofthe second collection of signals.

In an embodiment, the convolutional neural network can include a set offully-connected layers. At least the portion of the second collection ofsignals can be inputted into the set of fully-connected layers. The setof fully-connected layers can output a third collection of signals. Theone or more outputs from the convolutional neural network can begenerated based at least in part on at least a portion of the thirdcollection of signals.

In an embodiment, the at least one two-dimensional convolutionaloperation can utilize at least one two-dimensional filter to convolvethe representation of the video content. The representation of the videocontent can be reduced in signal size based at least in part on the atleast one two-dimensional convolutional operation.

In an embodiment, the at least one three-dimensional convolutionaloperation can utilize at least one three-dimensional filter to convolveat least the portion of the first collection of signals.

In an embodiment, the set of two-dimensional convolutional layers caninclude at least five two-dimensional convolutional layers. The set ofthree-dimensional convolutional layers can include at least threethree-dimensional convolutional layers.

In an embodiment, the convolutional neural network can be trained basedat least in part on the video content. The video content can beassociated with one or more labels for at least one of a recognizedscene, a recognized object, or a recognized action.

In an embodiment, the training of the convolutional neural network canfurther comprise determining one or more differences between the one ormore labels and the plurality of video feature descriptors. One or moreweight values of one or more filters associated with the convolutionalneural network can be adjusted to minimize the one or more differences.The adjusting of the one or more weight values can occur during abackpropagation through the convolutional neural network.

In an embodiment, the video feature descriptors can provide a first setof metrics indicating likelihoods that specified scenes are representedin the video content, a second set of metrics indicating likelihoodsthat specified objects are represented in the video content, and a thirdset of metrics indicating likelihoods that specified actions arerepresented in the video content.

It should be appreciated that many other features, applications,embodiments, and/or variations of the disclosed technology will beapparent from the accompanying drawings and from the following detaileddescription. Additional and/or alternative implementations of thestructures, systems, non-transitory computer readable media, and methodsdescribed herein can be employed without departing from the principlesof the disclosed technology.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 illustrates an example system including an example video featuredescriptor module configured to facilitate determining video featuredescriptors based on convolutional neural networks, according to anembodiment of the present disclosure.

FIG. 2 illustrates an example convolutional neural network moduleconfigured to facilitate determining video feature descriptors based onconvolutional neural networks, according to an embodiment of the presentdisclosure.

FIG. 3 illustrates an example scenario associated with determining videofeature descriptors based on convolutional neural networks, according toan embodiment of the present disclosure.

FIG. 4 illustrates an example flow associated with determining videofeature descriptors based on convolutional neural networks, according toan embodiment of the present disclosure.

FIG. 5 illustrates an example method associated with determining videofeature descriptors based on convolutional neural networks, according toan embodiment of the present disclosure.

FIG. 6 illustrates an example method associated with determining videofeature descriptors based on convolutional neural networks, according toan embodiment of the present disclosure.

FIG. 7 illustrates a network diagram of an example system including anexample social networking system that can be utilized in variousscenarios, according to an embodiment of the present disclosure.

FIG. 8 illustrates an example of a computer system or computing devicethat can be utilized in various scenarios, according to an embodiment ofthe present disclosure.

The figures depict various embodiments of the disclosed technology forpurposes of illustration only, wherein the figures use like referencenumerals to identify like elements. One skilled in the art will readilyrecognize from the following discussion that alternative embodiments ofthe structures and methods illustrated in the figures can be employedwithout departing from the principles of the disclosed technologydescribed herein.

DETAILED DESCRIPTION Determining Video Feature Descriptors Based onConvolutional Neural Networks

People use computing devices or systems for a wide variety of purposes.Computing devices can provide different kinds of functionality. Userscan utilize their computing devices to produce information, accessinformation, and share information. In some cases, computing devices caninclude or correspond to cameras capable of capturing or recording mediacontent, such as videos. Videos can generally include a set of images,such as a set of video image frames or still frames. Not only can videosrepresent or include scenes, items, subjects, or other objects, butvideos can also represent or include various movements, actions, orother changes in appearance over time.

Conventional approaches to recognizing objects in media contentgenerally involve analyzing image content in attempt to recognize oridentify any items, subjects, or other objects within the image content.In one example, conventional approaches to image object recognition canutilize two-dimensional convolutional neural networks. However,conventional two-dimensional convolutional neural networks can belimited in capability in that video content cannot be adequately orpractically analyzed in accordance with such conventional approaches. Itfollows that movements, motion, or other actions represented or includedin video content cannot be properly recognized under conventionalapproaches. Moreover, in some cases, conventional approaches can requiresignificant resources, such as time and processing power. As such, theseand other similar conventional approaches can be limiting, inaccurate,and inefficient.

Therefore, an improved approach can be beneficial for addressing oralleviating various concerns associated with conventional approaches.The disclosed technology can enable video feature descriptors to beacquired, calculated, identified, or otherwise determined based onconvolutional neural networks, which can also refer to deepconvolutional neural networks, deep networks, etc. Various embodimentsof the present disclosure can acquire video content for which videofeature descriptors are to be determined. The video content can beprocessed based at least in part on a convolutional neural networkincluding a set of two-dimensional convolutional layers and a set ofthree-dimensional convolutional layers. One or more outputs can begenerated from the convolutional neural network. A plurality of videofeature descriptors for the video content can be determined based atleast in part on the one or more outputs from the convolutional neuralnetwork. It is contemplated that there can be many variations and/orother possibilities.

FIG. 1 illustrates an example system 100 including an example videofeature descriptor module 102 configured to facilitate determining videofeature descriptors based on convolutional neural networks, according toan embodiment of the present disclosure, according to an embodiment ofthe present disclosure. As shown in the example of FIG. 1, the examplevideo feature descriptor 102 can include a video content acquisitionmodule 104 and a convolutional neural network module 106. In someinstances, the example system 100 can include at least one data store108. The components (e.g., modules, elements, etc.) shown in this figureand all figures herein are exemplary only, and other implementations mayinclude additional, fewer, integrated, or different components. Somecomponents may not be shown so as not to obscure relevant details.

In some embodiments, the video feature descriptor module 102 can beimplemented, in part or in whole, as software, hardware, or anycombination thereof. In general, a module as discussed herein can beassociated with software, hardware, or any combination thereof. In someimplementations, one or more functions, tasks, and/or operations ofmodules can be carried out or performed by software routines, softwareprocesses, hardware, and/or any combination thereof. In some cases, thevideo feature descriptor module 102 can be implemented, in part or inwhole, as software running on one or more computing devices or systems,such as on a user or client computing device. In one example, the videofeature descriptor module 102 or at least a portion thereof can beimplemented as or within an application (e.g., app), a program, or anapplet, etc., running on a user computing device or a client computingsystem, such as the user device 710 of FIG. 7. In another example, thevideo feature descriptor module 102 or at least a portion thereof can beimplemented using one or more computing devices or systems that includeone or more servers, such as network servers or cloud servers. In someinstances, the video feature descriptor module 102 can, in part or inwhole, be implemented within or configured to operate in conjunctionwith a social networking system (or service), such as the socialnetworking system 730 of FIG. 7.

The video content acquisition module 104 can be configured to facilitateacquiring video content for which video feature descriptors are to bedetermined. In some instances, the video content acquisition module 104can acquire video content from the social networking system. In somecases, the video content acquisition module 104 can acquire videocontent from an online media content system (or service). In oneexample, a user of the social networking system and/or the online mediacontent system can upload, share, or otherwise provide a video. Thevideo content acquisition module 104 can acquire the user's video, andthe video feature descriptor module 102 can facilitate determining videofeature descriptors for the user's video.

In some embodiments, video content can be stored at the at least onedata store 108. In some instances, the video feature descriptor module102 can be configured to communicate and/or operate with the at leastone data store 108, as shown in the example system 100. The at least onedata store 108 can be configured to store and maintain various types ofdata. In some implementations, the at least one data store 108 can storeinformation associated with the social networking system (e.g., thesocial networking system 730 of FIG. 7). The information associated withthe social networking system can include data about users, socialconnections, social interactions, locations, geo-fenced areas, maps,places, events, pages, groups, posts, communications, content, feeds,account settings, privacy settings, a social graph, and various othertypes of data. In some implementations, the at least one data store 108can store information associated with users, such as user identifiers,user information, profile information, user specified settings, contentproduced or posted by users, and various other types of user data. Insome embodiments, the at least one data store 108 can store mediacontent including video content, which can be acquired by the videocontent acquisition module 104. In some cases, the at least one datastore 108 can also store information associated with the video content,such as labels, tags, attributes, properties, and/or descriptions forthe video content. It should be appreciated that many variations arepossible.

The convolutional neural network module 106 can be configured tofacilitate processing the video content acquired by the video contentacquisition module 104. In some embodiments, the convolutional neuralnetwork module 106 can process the video content based at least in parton a convolutional neural network including a set of two-dimensionalconvolutional layers and a set of three-dimensional convolutionallayers. The convolutional neural network module 106 can also beconfigured to facilitate generating one or more outputs from theconvolutional neural network. The convolutional neural network module106 can also be configured to facilitate determining, based at least inpart on the one or more outputs from the convolutional neural network, aplurality of video feature descriptors for the video content. Moredetails regarding the convolutional neural network module 106 will beprovided below with reference to FIG. 2.

In one example, a video (e.g., a video file, a video clip, a videoportion, etc.) can be acquired by the video content acquisition module104. In this example, the video can include a cat jumping over a parkedbicycle in the woods. The convolutional neural network module 106 candevelop a convolutional neural network including a set of (one or more)two-dimensional convolutional layers and a set of (one or more)three-dimensional convolutional layers. The video can be processed basedat least in part on the convolutional neural network and outputs can begenerated or produced from the convolutional neural network. A pluralityof video feature descriptors for the video can be identified, defined,or otherwise determined based at least in part on the outputs. The videofeature descriptors can provide probabilities that certain scenes,objects, and/or actions (each of which can sometimes be referred to as a“concept”) are recognized or identified in the video.

Continuing with the previous example, the video feature descriptors forthe video can indicate a 84% probability that is video is associatedwith an outdoor scene, a 77% probability that the video is associatedwith a forest scene, a 26% probability that the video is associated withan indoor scene, a 23% probability that the video is associated with aliving room scene, and so forth. Moreover, in this example, the videofeature descriptors for the video can indicate a 96% probability that acat object is in the video, a 24% probability that a dog object is inthe video, a 79% probability that a tree object is in the video, a 88%probability that a bicycle object is in the video, a 12% probabilitythat a person object is in the video, and so forth. In this example, thevideo feature descriptors can also indicate a 81% probability of ajumping action being in the video, a 33% probability of a joggingaction, a 55% probably of a walking action, and so forth. In someinstances, the video feature descriptors (e.g., probabilities) of thevideo can be utilized in further processing of the video, such as whencategorizing, sorting, labeling, and/or tagging the video. It should beunderstood that this example and the specific details in the example areprovided for illustrative purposes and that there can be many variationsand other possibilities. For example, in some cases, there can behundreds or thousands of video feature descriptors for various scenes,objects, actions, or other concepts.

FIG. 2 illustrates an example convolutional neural network module 202configured to facilitate determining video feature descriptors based onconvolutional neural networks, according to an embodiment of the presentdisclosure. In some embodiments, the convolutional neural network module106 of FIG. 1 can be implemented as the example convolutional neuralnetwork module 202. As shown in FIG. 2, the example convolutional neuralnetwork module 202 can include a two-dimensional convolution module 204,a three-dimensional convolution module 206, and a training module 208.

As discussed previously, in some embodiments, the convolutional neuralnetwork module 202 can develop (e.g., construct, maintain, train, etc.)a convolutional neural network including a set of one or moretwo-dimensional convolutional layers and a set of one or morethree-dimensional convolutional layers. The convolutional neural networkmodule 202 can utilize the two-dimensional convolution module 204 toconstruct, develop, and/or maintain the set of two-dimensionalconvolutional layers and can utilize the three-dimensional convolutionmodule 206 to construct, develop, and/or maintain the set ofthree-dimensional convolutional layers.

As discussed, the convolutional neural network module 202 can facilitateprocessing video content based (at least in part) on the convolutionalneural network. For example, the video content can be forward propagatedthrough the convolutional neural network in an inference process togenerate one or more outputs from the convolutional neural network. Theconvolutional neural network module 202 can further determine, identify,or define a plurality of video feature descriptors for the video contentbased on the outputs. In some instances, the video feature descriptorscan provide a respective probability percentage, for each concept in alist of predefined or preset concepts (e.g., scenes, objects, actions,etc.), corresponding to a respective confidence indicating whether eachconcept is likely recognized in the video content or not. The videofeature descriptors can, for example, provide a first set of metricsindicating likelihoods that specified scenes are represented in thevideo content, a second set of metrics indicating likelihoods thatspecified objects are represented in the video content, and a third setof metrics indicating likelihoods that specified actions are representedin the video content. A more detailed discussion regarding theprocessing of the video content based at least in part on theconvolutional neural network will be provided below with reference toFIG. 3.

Furthermore, the training module 208 of the convolutional neural networkmodule 202 can be configured to facilitate training the convolutionalneural network. In some cases, training video content can be received orotherwise acquired. As such, the training module 208 can train theconvolutional neural network based at least in part on the trainingvideo content. The training video content can be known, verified, orconfirmed to include, represent, or capture certain scenes, objects,and/or actions. For example, the training video content can beassociated with one or more labels (e.g., social tags, descriptive tags,hashtags, etc.) for at least one of a recognized scene, a recognizedobject, or a recognized action. Based (at least in part) on the trainingvideo content being processed through the convolutional neural network,a plurality of video feature descriptors can be determined for thetraining video content. The determined plurality of video featuredescriptors can be compared to the one or more labels that are known orconfirmed for the training video content. The training of theconvolutional neural network can cause the determined video featuredescriptor to be closer to the known labels or expected results (i.e.,ground truth). It is contemplated that many iterations of the trainingcan be performed with various training video content items.

In some instances, the training module 208 can be configured todetermine one or more differences between the one or more labels and thedetermined plurality of video feature descriptors. The training module208 can perform a backpropagation through the convolutional neuralnetwork. During the backpropagation, the training module 208 can adjustone or more weight values of one or more filters associated with theconvolutional neural network in order to minimize the one or moredifferences. Accordingly, over a number of training iterations, optimalor otherwise suitable weight values can be learned for the filters andthe convolutional neural network can be sufficiently trained. In someinstances, each weight value for a filter can correspond to a pixelvalue (e.g., RGB value, HEX code, etc.) of the filter. It should beappreciated that many variations are possible.

FIG. 3 illustrates an example scenario 300 associated with determiningvideo feature descriptors based on convolutional neural networks,according to an embodiment of the present disclosure. The examplescenario 300 illustrates a (trained) convolutional neural network 302configured to facilitate processing video content and determining videofeature descriptors for the video content.

As shown in FIG. 3, the example convolutional neural network 302 caninclude a set of two-dimensional convolutional layers 310 and a set ofthree-dimensional convolutional layers 320. In the example scenario 300,the set of two-dimensional convolutional layers 310 can include at leastfive two-dimensional convolutional layers, and the set ofthree-dimensional convolutional layers 320 can include at least threethree-dimensional convolutional layers. Moreover, in someimplementations, the convolutional neural network 302 can include a setof fully-connected layers 330. Furthermore, in some embodiments, theconvolutional neural network 302 can include a softmax layer 340. Atleast a portion of each layer can be connected with at least a portionof another layer and information can be transmitted through the layers.

In some cases, during a forward propagation through the convolutionalneural network, data representing video content can be inputted throughthe first (e.g., leftmost in FIG. 3) two-dimensional convolutional layerand a plurality of video feature descriptors can be determined for thevideo content. The plurality of video feature descriptors can includeone or more video feature descriptors for scenes 350, one or more videofeature descriptors for objects 360, and one or more video featuredescriptors for actions 370.

In the example scenario 300, a video 380 can be acquired. The video 380can include, or be represented by, a set of images (i.e., video imageframes, still frames, etc.). In this example, the video 380 can includeFrame 1 382, Frame 2 384, and all other frames through Frame N 386. Ifthe video 380 is recorded at 24 frames per second and if Frame N 386 isthe 48th frame, for example, then a normal playback length of the video380 should be two seconds. In some implementations, the convolutionalneural network 302 can be configured to receive as input video contenthaving 64 frames. It is contemplated that many variations are possible.

Moreover, the video 380 can be represented as a plurality oftwo-dimensional image frames. Each of the plurality of two-dimensionalimage frames can extend in a first spatial dimension (e.g., horizontalX-axis) and a second spatial dimension (e.g., vertical Y-axis). Theplurality of two-dimensional image frames can be temporally sorted, suchas from the first frame (e.g., Frame 1 382) to the last frame (e.g.,Frame N 386). A third dimension (e.g., depth Z-axis) can correspond to atime dimension with respect to which the plurality of two-dimensionalimage frames is temporally sorted.

In one example, a representation of the video 380 can be inputted intothe set of two-dimensional convolutional layers 310, such as via thefirst (or leftmost) two-dimensional convolutional layer. At least onetwo-dimensional convolutional operation can be applied, within the setof two-dimensional convolutional layers 310, to the representation ofthe video 380. The at least one two-dimensional convolutional operationcan utilize at least one two-dimensional filter to convolve therepresentation of the video content. As such, the representation of thevideo content can be reduced in signal size based at least in part onthe at least one two-dimensional convolutional operation. In some cases,the two-dimensional convolution module 204 of FIG. 2 can facilitateapplying the at least one two-dimensional convolutional operation.Moreover, in this example, each two-dimensional convolutional layer canapply a respective two-dimensional convolutional operation to itsreceived input signals and can generate output signals to be inputtedinto a next layer during forward propagation, where the convolutionaloperation causes the generated output signals to be reduced in sizerelative to the received input signals.

Continuing with the example, a first collection of signals can beoutputted from the set of two-dimensional convolutional layers 310, suchas via the last (or rightmost) two-dimensional convolutional layer. Atleast a portion of the first collection of signals can be inputted intothe set of three-dimensional convolutional layers 320, such as via thefirst (or leftmost) three-dimensional convolutional layer. In thisexample, the first collection of signals can be outputted from the last(or rightmost) two-dimensional convolutional layer and at least theportion of the first collection of signals can be inputted into thefirst (or leftmost) three-dimensional convolutional layer.

Further, at least one three-dimensional convolutional operation can beapplied, within the set of three-dimensional convolutional layers 320,to at least the portion of the first collection of signals. The at leastone three-dimensional convolutional operation can utilize at least onethree-dimensional filter to convolve at least the portion of the firstcollection of signals. In some cases, the three-dimensional convolutionmodule 206 of FIG. 2 can facilitate applying the at least onethree-dimensional convolutional operation. Moreover, in this example,each three-dimensional convolutional layer can apply a respectivethree-dimensional convolutional operation to its received input signalsand can generate output signals to be inputted into a next layer duringforward propagation.

Additionally, in this example, a second collection of signals can beoutputted from the set of three-dimensional convolutional layers 320,such as via the last (or rightmost) three-dimensional convolutionallayer. At least a portion of the second collection of signals can beinputted into the set of fully-connected layers 330, such as via thefirst (or leftmost) fully-connected layer. A third collection of signalscan be outputted from the set of fully-connected layers 330, such as viathe last (or rightmost) fully-connected layer. One or more outputs fromthe convolutional neural network 302 can be generated based at least inpart on at least a portion of the third collection of signals.Accordingly, the one or more outputs from the convolutional neuralnetwork 302 can also be dependent on at least the portion of the secondcollection of signals since at least the portion of the third collectionof signals can be dependent on at least the portion of the secondcollection of signals. In some cases, the one or more outputs can benormalized or otherwise suitably modified by the softmax layer 340.

Continuing with the previous example, based on the one or more outputsfrom the convolutional neural network 302, the plurality of videofeature descriptors for the video 380 (e.g., scene descriptors 350,object descriptors 360, action descriptors 370, etc.) can be determined.In this example, the inputted video 380 can correspond to a recording ofa car driving in a park. As such, the scene descriptors 350 can, forinstance, indicate significant likelihoods that an outdoor scene and apark scene are recognized in the video 380 but lower likelihoods that anindoor scene and an office scene are recognized in the video 380. Theobject descriptors 360 can, for example, indicate significantlikelihoods that a tree object and a car object are recognized but lowerlikelihoods for a cat object and a table object. Also, in this example,the action descriptors 370 can indicate a significant likelihood that adriving action is recognized in the video 380 but lower likelihoods fora smiling action, a jumping action, and a walking object. It should benoted that this example scenario 300 and its specific details areprovided for illustrative purposes. Many variations are possible.

FIG. 4 illustrates an example flow 400 associated with determining videofeature descriptors based on convolutional neural networks, according toan embodiment of the present disclosure. At block 402, the example flow400 can take as input 64 frames of a video having a frame size of 227pixels tall by 227 pixels wide with 3 color pixel values (e.g., RGBvalues). A first convolutional operation can be performed using a filtersize of 11 pixels tall by 11 pixels wide and 3 frames in depth (i.e.,the filter has 3 frames of 11 pixels by 11 pixels each). In thisexample, the first convolutional operation can utilize 96 filters andcan employ a stride of 1×4×4 for each filter. At block 404, an output ofthe first convolutional operation can be determined. A first poolingoperation with a filter size of 3×3×3 and a stride of 2×2×2 can beapplied to the output of the first convolutional operation. At block406, an output of the first pooling operation can be determined. In somecases, pooling operations can cause signals to be translationalinvariant. In some embodiments, max pooling operations can be utilized.Further, as shown in the example flow 400, additional convolutionaloperations and pooling operations can be subsequently performed toproduce 2048 outputs, at block 408. At block 410, the outputs can benormalized, for example, by using a softmax process.

Moreover, in some embodiments, the quantities of filters, poolingoperations, and/or convolutional operations can be specified orselected. In some implementations, frame sizes, filter sizes, poolingsizes, and stride values can be specified or selected. Furthermore, insome cases, the quantity of descriptors can be specified or selected aswell. In one example, 4096 descriptors can be used. Again, it should benoted that this example and its specific details are provided forillustrative purposes. There can be many variations and otherpossibilities.

FIG. 5 illustrates an example method 500 associated with determiningvideo feature descriptors based on convolutional neural networks,according to an embodiment of the present disclosure. It should beappreciated that there can be additional, fewer, or alternative stepsperformed in similar or alternative orders, or in parallel, within thescope of the various embodiments unless otherwise stated.

At block 502, the example method 500 can acquire video content for whichvideo feature descriptors are to be determined. At block 504, theexample method 500 can process the video content based at least in parton a convolutional neural network including a set of two-dimensionalconvolutional layers and a set of three-dimensional convolutionallayers. At block 506, the example method 500 can generate one or moreoutputs from the convolutional neural network. At block 508, the examplemethod 500 can determine, based at least in part on the one or moreoutputs from the convolutional neural network, a plurality of videofeature descriptors for the video content.

FIG. 6 illustrates an example method 600 associated with determiningvideo feature descriptors based on convolutional neural networks,according to an embodiment of the present disclosure. Again, it shouldbe understood that there can be additional, fewer, or alternative stepsperformed in similar or alternative orders, or in parallel, within thescope of the various embodiments unless otherwise stated.

The example method 600 can facilitate the processing of the videocontent based at least in part on the convolutional neural network. Atblock 602, the example method 600 can input a representation of thevideo content into the set of two-dimensional convolutional layers. Atblock 604, the example method 600 can apply, within the set oftwo-dimensional convolutional layers, at least one two-dimensionalconvolutional operation to the representation of the video content. Atblock 606, the example method 600 can output a first collection ofsignals from the set of two-dimensional convolutional layers. At block608, the example method 600 can input at least a portion of the firstcollection of signals into the set of three-dimensional convolutionallayers. At block 610, the example method 600 can apply, within the setof three-dimensional convolutional layers, at least onethree-dimensional convolutional operation to at least the portion of thefirst collection of signals. At block 612, the example method 600 canoutput a second collection of signals from the set of three-dimensionalconvolutional layers. The one or more outputs from the convolutionalneural network can be dependent on at least a portion of the secondcollection of signals.

It is contemplated that there can be many other uses, applications,and/or variations associated with the various embodiments of the presentdisclosure. For example, in some cases, user can choose whether or notto opt-in to utilize the disclosed technology. The disclosed technologycan also ensure that various privacy settings and preferences aremaintained and can prevent private information from being divulged. Inanother example, various embodiments of the present disclosure canlearn, improve, and/or be refined over time.

Social Networking System—Example Implementation

FIG. 7 illustrates a network diagram of an example system 700 that canbe utilized in various scenarios, in accordance with an embodiment ofthe present disclosure. The system 700 includes one or more user devices710, one or more external systems 720, a social networking system (orservice) 730, and a network 750. In an embodiment, the social networkingservice, provider, and/or system discussed in connection with theembodiments described above may be implemented as the social networkingsystem 730. For purposes of illustration, the embodiment of the system700, shown by FIG. 7, includes a single external system 720 and a singleuser device 710. However, in other embodiments, the system 700 mayinclude more user devices 710 and/or more external systems 720. Incertain embodiments, the social networking system 730 is operated by asocial network provider, whereas the external systems 720 are separatefrom the social networking system 730 in that they may be operated bydifferent entities. In various embodiments, however, the socialnetworking system 730 and the external systems 720 operate inconjunction to provide social networking services to users (or members)of the social networking system 730. In this sense, the socialnetworking system 730 provides a platform or backbone, which othersystems, such as external systems 720, may use to provide socialnetworking services and functionalities to users across the Internet.

The user device 710 comprises one or more computing devices (or systems)that can receive input from a user and transmit and receive data via thenetwork 750. In one embodiment, the user device 710 is a conventionalcomputer system executing, for example, a Microsoft Windows compatibleoperating system (OS), Apple OS X, and/or a Linux distribution. Inanother embodiment, the user device 710 can be a computing device or adevice having computer functionality, such as a smart-phone, a tablet, apersonal digital assistant (PDA), a mobile telephone, a laptop computer,a wearable device (e.g., a pair of glasses, a watch, a bracelet, etc.),a camera, an appliance, etc. The user device 710 is configured tocommunicate via the network 750. The user device 710 can execute anapplication, for example, a browser application that allows a user ofthe user device 710 to interact with the social networking system 730.In another embodiment, the user device 710 interacts with the socialnetworking system 730 through an application programming interface (API)provided by the native operating system of the user device 710, such asiOS and ANDROID. The user device 710 is configured to communicate withthe external system 720 and the social networking system 730 via thenetwork 750, which may comprise any combination of local area and/orwide area networks, using wired and/or wireless communication systems.

In one embodiment, the network 750 uses standard communicationstechnologies and protocols. Thus, the network 750 can include linksusing technologies such as Ethernet, 702.11, worldwide interoperabilityfor microwave access (WiMAX), 3G, 4G, CDMA, GSM, LTE, digital subscriberline (DSL), etc. Similarly, the networking protocols used on the network750 can include multiprotocol label switching (MPLS), transmissioncontrol protocol/Internet protocol (TCP/IP), User Datagram Protocol(UDP), hypertext transport protocol (HTTP), simple mail transferprotocol (SMTP), file transfer protocol (FTP), and the like. The dataexchanged over the network 750 can be represented using technologiesand/or formats including hypertext markup language (HTML) and extensiblemarkup language (XML). In addition, all or some links can be encryptedusing conventional encryption technologies such as secure sockets layer(SSL), transport layer security (TLS), and Internet Protocol security(IPsec).

In one embodiment, the user device 710 may display content from theexternal system 720 and/or from the social networking system 730 byprocessing a markup language document 714 received from the externalsystem 720 and from the social networking system 730 using a browserapplication 712. The markup language document 714 identifies content andone or more instructions describing formatting or presentation of thecontent. By executing the instructions included in the markup languagedocument 714, the browser application 712 displays the identifiedcontent using the format or presentation described by the markuplanguage document 714. For example, the markup language document 714includes instructions for generating and displaying a web page havingmultiple frames that include text and/or image data retrieved from theexternal system 720 and the social networking system 730. In variousembodiments, the markup language document 714 comprises a data fileincluding extensible markup language (XML) data, extensible hypertextmarkup language (XHTML) data, or other markup language data.Additionally, the markup language document 714 may include JavaScriptObject Notation (JSON) data, JSON with padding (JSONP), and JavaScriptdata to facilitate data-interchange between the external system 720 andthe user device 710. The browser application 712 on the user device 710may use a JavaScript compiler to decode the markup language document714.

The markup language document 714 may also include, or link to,applications or application frameworks such as FLASH™ or Unity™applications, the SilverLight™ application framework, etc.

In one embodiment, the user device 710 also includes one or more cookies716 including data indicating whether a user of the user device 710 islogged into the social networking system 730, which may enablemodification of the data communicated from the social networking system730 to the user device 710.

The external system 720 includes one or more web servers that includeone or more web pages 722 a, 722 b, which are communicated to the userdevice 710 using the network 750. The external system 720 is separatefrom the social networking system 730. For example, the external system720 is associated with a first domain, while the social networkingsystem 730 is associated with a separate social networking domain. Webpages 722 a, 722 b, included in the external system 720, comprise markuplanguage documents 714 identifying content and including instructionsspecifying formatting or presentation of the identified content.

The social networking system 730 includes one or more computing devicesfor a social network, including a plurality of users, and providingusers of the social network with the ability to communicate and interactwith other users of the social network. In some instances, the socialnetwork can be represented by a graph, i.e., a data structure includingedges and nodes. Other data structures can also be used to represent thesocial network, including but not limited to databases, objects,classes, meta elements, files, or any other data structure. The socialnetworking system 730 may be administered, managed, or controlled by anoperator. The operator of the social networking system 730 may be ahuman being, an automated application, or a series of applications formanaging content, regulating policies, and collecting usage metricswithin the social networking system 730. Any type of operator may beused.

Users may join the social networking system 730 and then add connectionsto any number of other users of the social networking system 730 to whomthey desire to be connected. As used herein, the term “friend” refers toany other user of the social networking system 730 to whom a user hasformed a connection, association, or relationship via the socialnetworking system 730. For example, in an embodiment, if users in thesocial networking system 730 are represented as nodes in the socialgraph, the term “friend” can refer to an edge formed between anddirectly connecting two user nodes.

Connections may be added explicitly by a user or may be automaticallycreated by the social networking system 730 based on commoncharacteristics of the users (e.g., users who are alumni of the sameeducational institution). For example, a first user specifically selectsa particular other user to be a friend. Connections in the socialnetworking system 730 are usually in both directions, but need not be,so the terms “user” and “friend” depend on the frame of reference.Connections between users of the social networking system 730 areusually bilateral (“two-way”), or “mutual,” but connections may also beunilateral, or “one-way.” For example, if Bob and Joe are both users ofthe social networking system 730 and connected to each other, Bob andJoe are each other's connections. If, on the other hand, Bob wishes toconnect to Joe to view data communicated to the social networking system730 by Joe, but Joe does not wish to form a mutual connection, aunilateral connection may be established. The connection between usersmay be a direct connection; however, some embodiments of the socialnetworking system 730 allow the connection to be indirect via one ormore levels of connections or degrees of separation.

In addition to establishing and maintaining connections between usersand allowing interactions between users, the social networking system730 provides users with the ability to take actions on various types ofitems supported by the social networking system 730. These items mayinclude groups or networks (i.e., social networks of people, entities,and concepts) to which users of the social networking system 730 maybelong, events or calendar entries in which a user might be interested,computer-based applications that a user may use via the socialnetworking system 730, transactions that allow users to buy or sellitems via services provided by or through the social networking system730, and interactions with advertisements that a user may perform on oroff the social networking system 730. These are just a few examples ofthe items upon which a user may act on the social networking system 730,and many others are possible. A user may interact with anything that iscapable of being represented in the social networking system 730 or inthe external system 720, separate from the social networking system 730,or coupled to the social networking system 730 via the network 750.

The social networking system 730 is also capable of linking a variety ofentities. For example, the social networking system 730 enables users tointeract with each other as well as external systems 720 or otherentities through an API, a web service, or other communication channels.The social networking system 730 generates and maintains the “socialgraph” comprising a plurality of nodes interconnected by a plurality ofedges. Each node in the social graph may represent an entity that canact on another node and/or that can be acted on by another node. Thesocial graph may include various types of nodes. Examples of types ofnodes include users, non-person entities, content items, web pages,groups, activities, messages, concepts, and any other things that can berepresented by an object in the social networking system 730. An edgebetween two nodes in the social graph may represent a particular kind ofconnection, or association, between the two nodes, which may result fromnode relationships or from an action that was performed by one of thenodes on the other node. In some cases, the edges between nodes can beweighted. The weight of an edge can represent an attribute associatedwith the edge, such as a strength of the connection or associationbetween nodes. Different types of edges can be provided with differentweights. For example, an edge created when one user “likes” another usermay be given one weight, while an edge created when a user befriendsanother user may be given a different weight.

As an example, when a first user identifies a second user as a friend,an edge in the social graph is generated connecting a node representingthe first user and a second node representing the second user. Asvarious nodes relate or interact with each other, the social networkingsystem 730 modifies edges connecting the various nodes to reflect therelationships and interactions.

The social networking system 730 also includes user-generated content,which enhances a user's interactions with the social networking system730. User-generated content may include anything a user can add, upload,send, or “post” to the social networking system 730. For example, a usercommunicates posts to the social networking system 730 from a userdevice 710. Posts may include data such as status updates or othertextual data, location information, images such as photos, videos,links, music or other similar data and/or media. Content may also beadded to the social networking system 730 by a third party. Content“items” are represented as objects in the social networking system 730.In this way, users of the social networking system 730 are encouraged tocommunicate with each other by posting text and content items of varioustypes of media through various communication channels. Suchcommunication increases the interaction of users with each other andincreases the frequency with which users interact with the socialnetworking system 730.

The social networking system 730 includes a web server 732, an APIrequest server 734, a user profile store 736, a connection store 738, anaction logger 740, an activity log 742, and an authorization server 744.In an embodiment of the invention, the social networking system 730 mayinclude additional, fewer, or different components for variousapplications. Other components, such as network interfaces, securitymechanisms, load balancers, failover servers, management and networkoperations consoles, and the like are not shown so as to not obscure thedetails of the system.

The user profile store 736 maintains information about user accounts,including biographic, demographic, and other types of descriptiveinformation, such as work experience, educational history, hobbies orpreferences, location, and the like that has been declared by users orinferred by the social networking system 730. This information is storedin the user profile store 736 such that each user is uniquelyidentified. The social networking system 730 also stores data describingone or more connections between different users in the connection store738. The connection information may indicate users who have similar orcommon work experience, group memberships, hobbies, or educationalhistory. Additionally, the social networking system 730 includesuser-defined connections between different users, allowing users tospecify their relationships with other users. For example, user-definedconnections allow users to generate relationships with other users thatparallel the users' real-life relationships, such as friends,co-workers, partners, and so forth. Users may select from predefinedtypes of connections, or define their own connection types as needed.Connections with other nodes in the social networking system 730, suchas non-person entities, buckets, cluster centers, images, interests,pages, external systems, concepts, and the like are also stored in theconnection store 738.

The social networking system 730 maintains data about objects with whicha user may interact. To maintain this data, the user profile store 736and the connection store 738 store instances of the corresponding typeof objects maintained by the social networking system 730. Each objecttype has information fields that are suitable for storing informationappropriate to the type of object. For example, the user profile store736 contains data structures with fields suitable for describing auser's account and information related to a user's account. When a newobject of a particular type is created, the social networking system 730initializes a new data structure of the corresponding type, assigns aunique object identifier to it, and begins to add data to the object asneeded. This might occur, for example, when a user becomes a user of thesocial networking system 730, the social networking system 730 generatesa new instance of a user profile in the user profile store 736, assignsa unique identifier to the user account, and begins to populate thefields of the user account with information provided by the user.

The connection store 738 includes data structures suitable fordescribing a user's connections to other users, connections to externalsystems 720 or connections to other entities. The connection store 738may also associate a connection type with a user's connections, whichmay be used in conjunction with the user's privacy setting to regulateaccess to information about the user. In an embodiment of the invention,the user profile store 736 and the connection store 738 may beimplemented as a federated database.

Data stored in the connection store 738, the user profile store 736, andthe activity log 742 enables the social networking system 730 togenerate the social graph that uses nodes to identify various objectsand edges connecting nodes to identify relationships between differentobjects. For example, if a first user establishes a connection with asecond user in the social networking system 730, user accounts of thefirst user and the second user from the user profile store 736 may actas nodes in the social graph. The connection between the first user andthe second user stored by the connection store 738 is an edge betweenthe nodes associated with the first user and the second user. Continuingthis example, the second user may then send the first user a messagewithin the social networking system 730. The action of sending themessage, which may be stored, is another edge between the two nodes inthe social graph representing the first user and the second user.Additionally, the message itself may be identified and included in thesocial graph as another node connected to the nodes representing thefirst user and the second user.

In another example, a first user may tag a second user in an image thatis maintained by the social networking system 730 (or, alternatively, inan image maintained by another system outside of the social networkingsystem 730). The image may itself be represented as a node in the socialnetworking system 730. This tagging action may create edges between thefirst user and the second user as well as create an edge between each ofthe users and the image, which is also a node in the social graph. Inyet another example, if a user confirms attending an event, the user andthe event are nodes obtained from the user profile store 736, where theattendance of the event is an edge between the nodes that may beretrieved from the activity log 742. By generating and maintaining thesocial graph, the social networking system 730 includes data describingmany different types of objects and the interactions and connectionsamong those objects, providing a rich source of socially relevantinformation.

The web server 732 links the social networking system 730 to one or moreuser devices 710 and/or one or more external systems 720 via the network750. The web server 732 serves web pages, as well as other web-relatedcontent, such as Java, JavaScript, Flash, XML, and so forth. The webserver 732 may include a mail server or other messaging functionalityfor receiving and routing messages between the social networking system730 and one or more user devices 710. The messages can be instantmessages, queued messages (e.g., email), text and SMS messages, or anyother suitable messaging format.

The API request server 734 allows one or more external systems 720 anduser devices 710 to call access information from the social networkingsystem 730 by calling one or more API functions. The API request server734 may also allow external systems 720 to send information to thesocial networking system 730 by calling APIs. The external system 720,in one embodiment, sends an API request to the social networking system730 via the network 750, and the API request server 734 receives the APIrequest. The API request server 734 processes the request by calling anAPI associated with the API request to generate an appropriate response,which the API request server 734 communicates to the external system 720via the network 750. For example, responsive to an API request, the APIrequest server 734 collects data associated with a user, such as theuser's connections that have logged into the external system 720, andcommunicates the collected data to the external system 720. In anotherembodiment, the user device 710 communicates with the social networkingsystem 730 via APIs in the same manner as external systems 720.

The action logger 740 is capable of receiving communications from theweb server 732 about user actions on and/or off the social networkingsystem 730. The action logger 740 populates the activity log 742 withinformation about user actions, enabling the social networking system730 to discover various actions taken by its users within the socialnetworking system 730 and outside of the social networking system 730.Any action that a particular user takes with respect to another node onthe social networking system 730 may be associated with each user'saccount, through information maintained in the activity log 742 or in asimilar database or other data repository. Examples of actions taken bya user within the social networking system 730 that are identified andstored may include, for example, adding a connection to another user,sending a message to another user, reading a message from another user,viewing content associated with another user, attending an event postedby another user, posting an image, attempting to post an image, or otheractions interacting with another user or another object. When a usertakes an action within the social networking system 730, the action isrecorded in the activity log 742. In one embodiment, the socialnetworking system 730 maintains the activity log 742 as a database ofentries. When an action is taken within the social networking system730, an entry for the action is added to the activity log 742. Theactivity log 742 may be referred to as an action log.

Additionally, user actions may be associated with concepts and actionsthat occur within an entity outside of the social networking system 730,such as an external system 720 that is separate from the socialnetworking system 730. For example, the action logger 740 may receivedata describing a user's interaction with an external system 720 fromthe web server 732. In this example, the external system 720 reports auser's interaction according to structured actions and objects in thesocial graph.

Other examples of actions where a user interacts with an external system720 include a user expressing an interest in an external system 720 oranother entity, a user posting a comment to the social networking system730 that discusses an external system 720 or a web page 722 a within theexternal system 720, a user posting to the social networking system 730a Uniform Resource Locator (URL) or other identifier associated with anexternal system 720, a user attending an event associated with anexternal system 720, or any other action by a user that is related to anexternal system 720. Thus, the activity log 742 may include actionsdescribing interactions between a user of the social networking system730 and an external system 720 that is separate from the socialnetworking system 730.

The authorization server 744 enforces one or more privacy settings ofthe users of the social networking system 730. A privacy setting of auser determines how particular information associated with a user can beshared. The privacy setting comprises the specification of particularinformation associated with a user and the specification of the entityor entities with whom the information can be shared. Examples ofentities with which information can be shared may include other users,applications, external systems 720, or any entity that can potentiallyaccess the information. The information that can be shared by a usercomprises user account information, such as profile photos, phonenumbers associated with the user, user's connections, actions taken bythe user such as adding a connection, changing user profile information,and the like.

The privacy setting specification may be provided at different levels ofgranularity. For example, the privacy setting may identify specificinformation to be shared with other users; the privacy settingidentifies a work phone number or a specific set of related information,such as, personal information including profile photo, home phonenumber, and status. Alternatively, the privacy setting may apply to allthe information associated with the user. The specification of the setof entities that can access particular information can also be specifiedat various levels of granularity. Various sets of entities with whichinformation can be shared may include, for example, all friends of theuser, all friends of friends, all applications, or all external systems720. One embodiment allows the specification of the set of entities tocomprise an enumeration of entities. For example, the user may provide alist of external systems 720 that are allowed to access certaininformation. Another embodiment allows the specification to comprise aset of entities along with exceptions that are not allowed to access theinformation. For example, a user may allow all external systems 720 toaccess the user's work information, but specify a list of externalsystems 720 that are not allowed to access the work information. Certainembodiments call the list of exceptions that are not allowed to accesscertain information a “block list”. External systems 720 belonging to ablock list specified by a user are blocked from accessing theinformation specified in the privacy setting. Various combinations ofgranularity of specification of information, and granularity ofspecification of entities, with which information is shared arepossible. For example, all personal information may be shared withfriends whereas all work information may be shared with friends offriends.

The authorization server 744 contains logic to determine if certaininformation associated with a user can be accessed by a user's friends,external systems 720, and/or other applications and entities. Theexternal system 720 may need authorization from the authorization server744 to access the user's more private and sensitive information, such asthe user's work phone number. Based on the user's privacy settings, theauthorization server 744 determines if another user, the external system720, an application, or another entity is allowed to access informationassociated with the user, including information about actions taken bythe user.

In some embodiments, the social networking system 730 can include avideo feature descriptor module 746. The video feature descriptor module746 can, for example, be implemented as the video feature descriptormodule 102 of FIG. 1. As discussed previously, it should be appreciatedthat there can be many variations and other possibilities. Otherfeatures of the video feature descriptor module 746 are discussed hereinin connection with the video feature descriptor module 102.

Hardware Implementation

The foregoing processes and features can be implemented by a widevariety of machine and computer system architectures and in a widevariety of network and computing environments. FIG. 8 illustrates anexample of a computer system 800 that may be used to implement one ormore of the embodiments described herein in accordance with anembodiment of the invention. The computer system 800 includes sets ofinstructions for causing the computer system 800 to perform theprocesses and features discussed herein. The computer system 800 may beconnected (e.g., networked) to other machines. In a networkeddeployment, the computer system 800 may operate in the capacity of aserver machine or a client machine in a client-server networkenvironment, or as a peer machine in a peer-to-peer (or distributed)network environment. In an embodiment of the invention, the computersystem 800 may be the social networking system 730, the user device 710,and the external system 820, or a component thereof. In an embodiment ofthe invention, the computer system 800 may be one server among many thatconstitutes all or part of the social networking system 730.

The computer system 800 includes a processor 802, a cache 804, and oneor more executable modules and drivers, stored on a computer-readablemedium, directed to the processes and features described herein.Additionally, the computer system 800 includes a high performanceinput/output (I/O) bus 806 and a standard I/O bus 808. A host bridge 810couples processor 802 to high performance I/O bus 806, whereas I/O busbridge 812 couples the two buses 806 and 808 to each other. A systemmemory 814 and one or more network interfaces 816 couple to highperformance I/O bus 806. The computer system 800 may further includevideo memory and a display device coupled to the video memory (notshown). Mass storage 818 and I/O ports 820 couple to the standard I/Obus 808. The computer system 800 may optionally include a keyboard andpointing device, a display device, or other input/output devices (notshown) coupled to the standard I/O bus 808. Collectively, these elementsare intended to represent a broad category of computer hardware systems,including but not limited to computer systems based on thex86-compatible processors manufactured by Intel Corporation of SantaClara, Calif., and the x86-compatible processors manufactured byAdvanced Micro Devices (AMD), Inc., of Sunnyvale, Calif., as well as anyother suitable processor.

An operating system manages and controls the operation of the computersystem 800, including the input and output of data to and from softwareapplications (not shown). The operating system provides an interfacebetween the software applications being executed on the system and thehardware components of the system. Any suitable operating system may beused, such as the LINUX Operating System, the Apple Macintosh OperatingSystem, available from Apple Computer Inc. of Cupertino, Calif., UNIXoperating systems, Microsoft® Windows® operating systems, BSD operatingsystems, and the like. Other implementations are possible.

The elements of the computer system 800 are described in greater detailbelow. In particular, the network interface 816 provides communicationbetween the computer system 800 and any of a wide range of networks,such as an Ethernet (e.g., IEEE 802.3) network, a backplane, etc. Themass storage 818 provides permanent storage for the data and programminginstructions to perform the above-described processes and featuresimplemented by the respective computing systems identified above,whereas the system memory 814 (e.g., DRAM) provides temporary storagefor the data and programming instructions when executed by the processor802. The I/O ports 820 may be one or more serial and/or parallelcommunication ports that provide communication between additionalperipheral devices, which may be coupled to the computer system 800.

The computer system 800 may include a variety of system architectures,and various components of the computer system 800 may be rearranged. Forexample, the cache 804 may be on-chip with processor 802. Alternatively,the cache 804 and the processor 802 may be packed together as a“processor module”, with processor 802 being referred to as the“processor core”. Furthermore, certain embodiments of the invention mayneither require nor include all of the above components. For example,peripheral devices coupled to the standard I/O bus 808 may couple to thehigh performance I/O bus 806. In addition, in some embodiments, only asingle bus may exist, with the components of the computer system 800being coupled to the single bus. Moreover, the computer system 800 mayinclude additional components, such as additional processors, storagedevices, or memories.

In general, the processes and features described herein may beimplemented as part of an operating system or a specific application,component, program, object, module, or series of instructions referredto as “programs”. For example, one or more programs may be used toexecute specific processes described herein. The programs typicallycomprise one or more instructions in various memory and storage devicesin the computer system 800 that, when read and executed by one or moreprocessors, cause the computer system 800 to perform operations toexecute the processes and features described herein. The processes andfeatures described herein may be implemented in software, firmware,hardware (e.g., an application specific integrated circuit), or anycombination thereof.

In one implementation, the processes and features described herein areimplemented as a series of executable modules run by the computer system800, individually or collectively in a distributed computingenvironment. The foregoing modules may be realized by hardware,executable modules stored on a computer-readable medium (ormachine-readable medium), or a combination of both. For example, themodules may comprise a plurality or series of instructions to beexecuted by a processor in a hardware system, such as the processor 802.Initially, the series of instructions may be stored on a storage device,such as the mass storage 818. However, the series of instructions can bestored on any suitable computer readable storage medium. Furthermore,the series of instructions need not be stored locally, and could bereceived from a remote storage device, such as a server on a network,via the network interface 816. The instructions are copied from thestorage device, such as the mass storage 818, into the system memory 814and then accessed and executed by the processor 802. In variousimplementations, a module or modules can be executed by a processor ormultiple processors in one or multiple locations, such as multipleservers in a parallel processing environment.

Examples of computer-readable media include, but are not limited to,recordable type media such as volatile and non-volatile memory devices;solid state memories; floppy and other removable disks; hard diskdrives; magnetic media; optical disks (e.g., Compact Disk Read-OnlyMemory (CD ROMS), Digital Versatile Disks (DVDs)); other similarnon-transitory (or transitory), tangible (or non-tangible) storagemedium; or any type of medium suitable for storing, encoding, orcarrying a series of instructions for execution by the computer system800 to perform any one or more of the processes and features describedherein.

For purposes of explanation, numerous specific details are set forth inorder to provide a thorough understanding of the description. It will beapparent, however, to one skilled in the art that embodiments of thedisclosure can be practiced without these specific details. In someinstances, modules, structures, processes, features, and devices areshown in block diagram form in order to avoid obscuring the description.In other instances, functional block diagrams and flow diagrams areshown to represent data and logic flows. The components of blockdiagrams and flow diagrams (e.g., modules, blocks, structures, devices,features, etc.) may be variously combined, separated, removed,reordered, and replaced in a manner other than as expressly describedand depicted herein.

Reference in this specification to “one embodiment”, “an embodiment”,“other embodiments”, “one series of embodiments”, “some embodiments”,“various embodiments”, or the like means that a particular feature,design, structure, or characteristic described in connection with theembodiment is included in at least one embodiment of the disclosure. Theappearances of, for example, the phrase “in one embodiment” or “in anembodiment” in various places in the specification are not necessarilyall referring to the same embodiment, nor are separate or alternativeembodiments mutually exclusive of other embodiments. Moreover, whetheror not there is express reference to an “embodiment” or the like,various features are described, which may be variously combined andincluded in some embodiments, but also variously omitted in otherembodiments. Similarly, various features are described that may bepreferences or requirements for some embodiments, but not otherembodiments.

The language used herein has been principally selected for readabilityand instructional purposes, and it may not have been selected todelineate or circumscribe the inventive subject matter. It is thereforeintended that the scope of the invention be limited not by this detaileddescription, but rather by any claims that issue on an application basedhereon. Accordingly, the disclosure of the embodiments of the inventionis intended to be illustrative, but not limiting, of the scope of theinvention, which is set forth in the following claims.

What is claimed is:
 1. A computer-implemented method comprising:acquiring, by a computing system, video content for which video featuredescriptors are to be determined; processing, by the computing system,the video content based at least in part on a convolutional neuralnetwork including a set of two-dimensional convolutional layers and aset of three-dimensional convolutional layers; generating, by thecomputing system, one or more outputs from the convolutional neuralnetwork; and determining, by the computing system, based at least inpart on the one or more outputs from the convolutional neural network, aplurality of video feature descriptors for the video content.
 2. Thecomputer-implemented method of claim 1, wherein the video content isrepresented as a plurality of two-dimensional image frames, wherein eachof the plurality of two-dimensional image frames extends in a firstspatial dimension and a second spatial dimension, wherein the pluralityof two-dimensional image frames is temporally sorted, and wherein athird dimension corresponds to a time dimension with respect to whichthe plurality of two-dimensional image frames is temporally sorted. 3.The computer-implemented method of claim 1, wherein the processing ofthe video content based at least in part on the convolutional neuralnetwork further comprises: inputting a representation of the videocontent into the set of two-dimensional convolutional layers; applying,within the set of two-dimensional convolutional layers, at least onetwo-dimensional convolutional operation to the representation of thevideo content; outputting a first collection of signals from the set oftwo-dimensional convolutional layers; inputting at least a portion ofthe first collection of signals into the set of three-dimensionalconvolutional layers; applying, within the set of three-dimensionalconvolutional layers, at least one three-dimensional convolutionaloperation to at least the portion of the first collection of signals;and outputting a second collection of signals from the set ofthree-dimensional convolutional layers, wherein the one or more outputsfrom the convolutional neural network are dependent on at least aportion of the second collection of signals.
 4. The computer-implementedmethod of claim 3, wherein the convolutional neural network includes aset of fully-connected layers, wherein at least the portion of thesecond collection of signals is inputted into the set of fully-connectedlayers, wherein the set of fully-connected layers outputs a thirdcollection of signals, and wherein the one or more outputs from theconvolutional neural network are generated based at least in part on atleast a portion of the third collection of signals.
 5. Thecomputer-implemented method of claim 3, wherein the at least onetwo-dimensional convolutional operation utilizes at least onetwo-dimensional filter to convolve the representation of the videocontent, and wherein the representation of the video content is reducedin signal size based at least in part on the at least onetwo-dimensional convolutional operation.
 6. The computer-implementedmethod of claim 3, wherein the at least one three-dimensionalconvolutional operation utilizes at least one three-dimensional filterto convolve at least the portion of the first collection of signals. 7.The computer-implemented method of claim 1, wherein the set oftwo-dimensional convolutional layers includes at least fivetwo-dimensional convolutional layers, and wherein the set ofthree-dimensional convolutional layers includes at least threethree-dimensional convolutional layers.
 8. The computer-implementedmethod of claim 1, further comprising: training the convolutional neuralnetwork based at least in part on the video content, wherein the videocontent is associated with one or more labels for at least one of arecognized scene, a recognized object, or a recognized action.
 9. Thecomputer-implemented method of claim 8, wherein the training of theconvolutional neural network further comprises: determining one or moredifferences between the one or more labels and the plurality of videofeature descriptors; and adjusting one or more weight values of one ormore filters associated with the convolutional neural network tominimize the one or more differences, wherein the adjusting of the oneor more weight values occurs during a backpropagation through theconvolutional neural network.
 10. The computer-implemented method ofclaim 1, wherein the video feature descriptors provide a first set ofmetrics indicating likelihoods that specified scenes are represented inthe video content, a second set of metrics indicating likelihoods thatspecified objects are represented in the video content, and a third setof metrics indicating likelihoods that specified actions are representedin the video content.
 11. A system comprising: at least one processor;and a memory storing instructions that, when executed by the at leastone processor, cause the system to perform: acquiring video content forwhich video feature descriptors are to be determined; processing thevideo content based at least in part on a convolutional neural networkincluding a set of two-dimensional convolutional layers and a set ofthree-dimensional convolutional layers; generating one or more outputsfrom the convolutional neural network; and determining, based at leastin part on the one or more outputs from the convolutional neuralnetwork, a plurality of video feature descriptors for the video content.12. The system of claim 11, wherein the video content is represented asa plurality of two-dimensional image frames, wherein each of theplurality of two-dimensional image frames extends in a first spatialdimension and a second spatial dimension, wherein the plurality oftwo-dimensional image frames is temporally sorted, and wherein a thirddimension corresponds to a time dimension with respect to which theplurality of two-dimensional image frames is temporally sorted.
 13. Thesystem of claim 11, wherein the processing of the video content based atleast in part on the convolutional neural network further comprises:inputting a representation of the video content into the set oftwo-dimensional convolutional layers; applying, within the set oftwo-dimensional convolutional layers, at least one two-dimensionalconvolutional operation to the representation of the video content;outputting a first collection of signals from the set of two-dimensionalconvolutional layers; inputting at least a portion of the firstcollection of signals into the set of three-dimensional convolutionallayers; applying, within the set of three-dimensional convolutionallayers, at least one three-dimensional convolutional operation to atleast the portion of the first collection of signals; and outputting asecond collection of signals from the set of three-dimensionalconvolutional layers, wherein the one or more outputs from theconvolutional neural network are dependent on at least a portion of thesecond collection of signals.
 14. The system of claim 11, wherein theset of two-dimensional convolutional layers includes at least fivetwo-dimensional convolutional layers, and wherein the set ofthree-dimensional convolutional layers includes at least threethree-dimensional convolutional layers.
 15. The system of claim 11,wherein the video feature descriptors provide a first set of metricsindicating likelihoods that specified scenes are represented in thevideo content, a second set of metrics indicating likelihoods thatspecified objects are represented in the video content, and a third setof metrics indicating likelihoods that specified actions are representedin the video content.
 16. A non-transitory computer-readable storagemedium including instructions that, when executed by at least oneprocessor of a computing system, cause the computing system to perform:acquiring video content for which video feature descriptors are to bedetermined; processing the video content based at least in part on aconvolutional neural network including a set of two-dimensionalconvolutional layers and a set of three-dimensional convolutionallayers; generating one or more outputs from the convolutional neuralnetwork; and determining, based at least in part on the one or moreoutputs from the convolutional neural network, a plurality of videofeature descriptors for the video content.
 17. The non-transitorycomputer-readable storage medium of claim 16, wherein the video contentis represented as a plurality of two-dimensional image frames, whereineach of the plurality of two-dimensional image frames extends in a firstspatial dimension and a second spatial dimension, wherein the pluralityof two-dimensional image frames is temporally sorted, and wherein athird dimension corresponds to a time dimension with respect to whichthe plurality of two-dimensional image frames is temporally sorted. 18.The non-transitory computer-readable storage medium of claim 16, whereinthe processing of the video content based at least in part on theconvolutional neural network further comprises: inputting arepresentation of the video content into the set of two-dimensionalconvolutional layers; applying, within the set of two-dimensionalconvolutional layers, at least one two-dimensional convolutionaloperation to the representation of the video content; outputting a firstcollection of signals from the set of two-dimensional convolutionallayers; inputting at least a portion of the first collection of signalsinto the set of three-dimensional convolutional layers; applying, withinthe set of three-dimensional convolutional layers, at least onethree-dimensional convolutional operation to at least the portion of thefirst collection of signals; and outputting a second collection ofsignals from the set of three-dimensional convolutional layers, whereinthe one or more outputs from the convolutional neural network aredependent on at least a portion of the second collection of signals. 19.The non-transitory computer-readable storage medium of claim 16, whereinthe set of two-dimensional convolutional layers includes at least fivetwo-dimensional convolutional layers, and wherein the set ofthree-dimensional convolutional layers includes at least threethree-dimensional convolutional layers.
 20. The non-transitorycomputer-readable storage medium of claim 16, wherein the video featuredescriptors provide a first set of metrics indicating likelihoods thatspecified scenes are represented in the video content, a second set ofmetrics indicating likelihoods that specified objects are represented inthe video content, and a third set of metrics indicating likelihoodsthat specified actions are represented in the video content.